Dynamic Motion Control of Multi-Segment Soft Robots Using Piecewise Constant Curvature Matched with an Augmented Rigid Body Model
Name
RoSo_19__Dynamic_Motion_Control_of_Multi_Segment_Soft_Robots_Using_Piecewise_Constant_Curvature_Matched_with_an_Augmented_Rigid_Body_Model.pdf
Description
Accepted version
Size
7.25 MB
Format
Adobe PDF
Checksum (MD5)
8939238b0b52bbbda62a7519d9235c6e
Author(s)
Katzschmann, Robert K.
Santina, Cosimo Della
Toshimitsu, Yasunori
Bicchi, Antonio
Rus, Daniela
Date Issued
April 2019
Publisher
IEEE
Citation
Katzschmann, Robert K., Santina, Cosimo Della, Toshimitsu, Yasunori, Bicchi, Antonio and Rus, Daniela. 2019. "Dynamic Motion Control of Multi-Segment Soft Robots Using Piecewise Constant Curvature Matched with an Augmented Rigid Body Model."
Version
Author's final manuscript
Abstract
© 2019 IEEE. Despite the emergence of many soft-bodied robotic systems, model-based feedback control for soft robots has remained an open challenge. This is largely due to the intrinsic difficulties in designing controllers for systems with infinite dimensions. This work extends our previously proposed formulation for the dynamics of a soft robot from two to three dimensions. The formulation connects the soft robot's dynamic behavior to a rigid-bodied robot with parallel elastic actuation. The matching between the two systems is exact under the hypothesis of Piecewise Constant Curvature. Based on this connection, we introduce a control architecture with the aim of achieving accurate curvature and bending control. This controller accounts for the natural softness of the system moving in three dimensions, and for the dynamic forces acting on the system. The controller is validated in a realistic simulation, together with a kinematic inversion algorithm. The paper also introduces a soft robot capable of three-dimensional motion, that we use to experimentally validate our control strategy.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/robosoft.2019.8722799
